Automatic1111 CUDA out of memory fix: Solve VRAM Errors

Updated June 2026

Encountering ‘CUDA out of memory’ in Automatic1111 Stable Diffusion stops your AI generations cold. This error means your GPU has exhausted its Video Random Access Memory (VRAM), which is critical for processing large models and high-resolution images.

Why “CUDA Out of Memory” Happens

Encountering ‘CUDA out of memory’ in Automatic1111 Stable Diffusion stops your AI generations cold. This error means your GPU has exhausted its Video Random Access Memory (VRAM), which is critical for processing large models and high-resolution images. The typical error message looks like this:

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate XXX MiB (GPU XXX; XXX MiB total capacity; XXX MiB already allocated; XXX MiB free; XXX MiB reserved in total by PyTorch) If reserved memory is problematic, try setting max_split_size_mb to avoid fragmentation. See documentation for more information.

This issue commonly arises from:

• Low VRAM on your GPU (e.g., 8GB or less).
• Attempting very high-resolution image generation.
• Large batch sizes trying to generate multiple images simultaneously.
• Background applications consuming VRAM.
• Using particularly large Stable Diffusion models.

Immediate Fixes: Edit webui-user.bat

The fastest way to tackle VRAM issues is by adding commandline arguments to your webui-user.bat file, which instruct Automatic1111 to optimize memory usage.

1. Navigate to your Automatic1111 installation folder.
2. Right-click webui-user.bat and select “Edit” (use Notepad or any text editor).
3. Find the line set COMMANDLINE_ARGS=
4. Add one or more of the following arguments, separated by spaces.

Recommended Arguments:

• --lowvram: Aggressively reduces VRAM by processing in smaller chunks. Expect slower generation.
• --medvram: A moderate VRAM optimization, offering better performance than --lowvram.
• --xformers: If installed (NVIDIA only), this provides highly optimized attention mechanisms, speeding up generations and reducing VRAM. (Requires separate installation).
• --no-half: Forces full-precision (FP32) instead of half-precision (FP16). Uses more VRAM but can resolve errors on older GPUs. Only use if others fail.
• --opt-sdp-attention / --opt-sdp-no-mem-attention: Alternative VRAM optimizations. Try if --xformers isn’t an option.

Example: To use --medvram and --xformers:

set COMMANDLINE_ARGS=--medvram --xformers

5. Save the webui-user.bat file.
6. Run webui-user.bat to start Automatic1111 with the new settings.

Test your generations after each change. Start with --medvram, then --lowvram if needed. Always add --xformers if possible.

Optimize In-Software Settings

Adjust these Automatic1111 interface settings to further reduce VRAM consumption:

1. Reduce Image Resolution: Generating high-resolution images directly uses significant VRAM. Start with 512×512 or 768×768. Utilize “Hires. fix” (under txt2img) for upscaling, which is more VRAM efficient.
2. Decrease Batch Size/Count: Set “Batch size” (parallel images) to 1. Set “Batch count” (sequential batches) to 1. Generate images sequentially if you need multiples.
3. Choose Smaller Models: Large, unpruned Stable Diffusion models demand more VRAM. Opt for “pruned” or “half-precision (FP16)” versions of models.
4. Increase Clip Skip: In the “Settings” tab, increase “Clip Skip” (e.g., to 2 or 3) for slight VRAM reduction and speed benefits on some models.
5. Disable Unnecessary Extensions: Go to the “Extensions” tab, uncheck unused extensions, and “Apply and restart UI.”
6. Sampling Method: Some methods like Euler a, DPM++ 2M Karras, or UniPC are generally lighter on VRAM. Experiment with these.

Advanced Troubleshooting and System Checks

If memory errors persist, look at these broader system-level factors:

1. Close Background Applications: Shut down any other GPU-intensive software, browsers with many tabs, or games running in the background.
2. Update GPU Drivers: Outdated drivers can cause inefficiencies. Download and perform a clean installation of the latest drivers from NVIDIA or AMD’s official websites.
3. Increase Virtual Memory (Page File): If system RAM is limited, increasing your Windows page file size can help prevent system bottlenecks that indirectly impact GPU VRAM management. Search for “Adjust the appearance and performance of Windows,” go to “Advanced,” and then “Change…” under “Virtual memory.” Set a custom size (e.g., 1.5-2x your RAM) and restart your PC.

Diagnostic checklist before you escalate

Most web-app failures can be narrowed to service status, one account session, browser data, an extension, or the network. Test those boundaries in order rather than clearing everything at once. A private window and a second network are especially useful because they change one layer without altering your account data.

1. Check the provider’s official status page before changing local settings.
2. Hard-refresh, start a new session, and test a private browser window.
3. Disable content blockers, privacy extensions, VPN, proxy, and secure DNS temporarily.
4. Compare another browser, device, and network to locate the failing boundary.
5. Record timestamps, error text, and the smallest reproducible sequence for support.

Verify the recovery across session and network boundaries

When Automatic1111 CUDA out of memory starts working, repeat the original action in a fresh tab and then in the normal browser profile. Confirm that buttons, uploads, saved history, and live updates behave normally instead of only rendering the first screen. If private mode works but the regular profile fails, continue isolating cookies and extensions rather than declaring the service fixed.

Restore extensions, VPN, proxy, secure DNS, and content filtering one at a time. Reload after each change. This controlled restoration identifies the incompatible layer and prevents the common outcome where everything is disabled permanently. Finish by testing one other device or network so you know whether the recovery belongs to the account, the device, or the connection.

• The original action succeeds twice in a fresh session.
• The normal browser profile works after cleanup.
• Extensions and network controls are restored individually.
• Saved data and account history remain available.
• A second device or network confirms the result.

Keep a short note of the working configuration and the date of the test. Products, models, browser versions, limits, and safety policies change over time, so a previously successful workaround may later become obsolete. Prefer current official documentation over old forum instructions, and reverse temporary diagnostic changes once testing is complete. This gives you a reliable baseline without leaving extensions disabled, security controls weakened, or experimental settings enabled indefinitely. Recheck the baseline after major updates before assuming an older failure has returned for the same reason.

When none of the fixes work

Repeat the smallest failing action once and record the exact local time and time zone. Note the product, model or feature, account plan, browser or app version, operating system, and whether the same action works in a private window, on another device, or on another network. This evidence is much more useful than saying the tool is “still broken.”

Use the provider’s official support channel. Include a screenshot with sensitive information removed and list the steps already tested. For developer tools, add sanitized request and response details, correlation IDs, and SDK versions. Never send passwords, one-time codes, API keys, session cookies, private repository contents, or complete payment information.

FAQ

What is VRAM?

VRAM (Video Random Access Memory) is specialized, high-speed memory integrated directly onto your graphics card (GPU). It stores data vital for the GPU’s operations, such as textures and calculations. More VRAM allows the GPU to handle larger and more complex tasks without memory bottlenecks.

Can I use Automatic1111 without a dedicated GPU?

While technically possible with CPU-only modes or integrated graphics, the performance is extremely slow, often taking hours per image. For a practical and usable experience, a dedicated NVIDIA or AMD GPU with at least 8GB of VRAM is highly recommended.

Does updating my GPU drivers really help with “CUDA out of memory” errors?

Yes, it can significantly help. GPU driver updates often include optimizations for memory management, bug fixes, and performance enhancements. These improvements can either reduce VRAM consumption or allow your GPU to utilize its existing VRAM more efficiently, making driver updates a crucial first step in troubleshooting GPU-related issues.

By adjusting commandline arguments, optimizing in-software settings, and performing basic system checks, you can effectively resolve ‘CUDA out of memory’ errors in Automatic1111.

Bottom line: Work from the least disruptive test to the most specific one. Confirm service health, isolate session and network variables, then escalate with clean evidence instead of repeating the same failing action.